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Microsoft DP-600 Exam (page: 1)
Microsoft Implementing Analytics Solutions Using Fabric
Updated on: 12-Feb-2026

Viewing Page 1 of 26
DP-600 PDF 198 Questions

HOTSPOT (Drag and Drop is not supported)
You have a Fabric tenant.

You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power BI visuals. You run the following code.



For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: No
Create and render a quick visualize instance
Create a QuickVisualize instance from the DataFrame you created. If you're using a pandas DataFrame, you can use our utility function as shown in the following code snippet to create the report. If you're using a DataFrame other than pandas, parse the data yourself.

# Create a Power BI report from your data
PBI_visualize = QuickVisualize(get_dataset_config(df), auth=device_auth)
# Render new report PBI_visualize


Box 2: Yes
Box 3: Yes


Reference:

https://learn.microsoft.com/en-us/power-bi/create-reports/jupyter-quick-report



You are analyzing the data in a Fabric notebook.

You have a Spark DataFrame assigned to a variable named df.

You need to use the Chart view in the notebook to explore the data manually. Which function should you run to make the data available in the Chart view?

  1. displayHTML
  2. show
  3. write
  4. display

Answer(s): D

Explanation:

Built-in visualization command - display() function
The Fabric built-in visualization function allows you to turn Apache Spark DataFrames, Pandas DataFrames and SQL query results into rich format data visualizations.

You can use the display function on dataframes that created in PySpark and Scala on Spark DataFrames or Resilient Distributed Datasets (RDD) functions to produce the rich dataframe table view and chart view.

The output of SQL statement appears in the rendered table view by default.


Reference:

https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-visualization



You have a Fabric notebook that has the Python code and output shown in the following exhibit.



Which type of analytics are you performing?

  1. descriptive
  2. diagnostic
  3. prescriptive
  4. predictive

Answer(s): A

Explanation:

This is a histogram. Histogram are used in relation to descriptive statistics calculations.


Reference:

https://www.advantive.com/solutions/spc-software/quality-advisor/data-analysis-tools/histogram-calculate- descriptive-statistics/



You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake. The data contains details about customer demographics and product usage.

You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age, and customer tenure.

Which type of analytics are you performing?

  1. diagnostic
  2. descriptive
  3. prescriptive
  4. predictive

Answer(s): B

Explanation:

What is Customer Retention Analytics?
Customer retention analytics provide predictive metrics of which customers may churn, allowing businesses to prevent this from happening. Let us understand this by an example, by using customer retention analytics, companies can reduce churn and increase profits, as evidenced by a McKinsey report suggesting that extensive use of customer data analytics can drive profit. Customer retention metrics, including the customer retention rate, are used to measure the likelihood of retaining and attracting customers to a business. This is how data analytics helps in customer retention.

Descriptive Analytics
Descriptive analytics provide you with granular insights based on historical data. This includes tracking past purchases, customer complaints, customer service reviews, and more. In order to implement descriptive customer retention analytics, your cloud engineers would need to make sure all customer data is on-premise and up-to-date and backed up on a regular basis. Because it uses historical data to create retention strategies and personalize customer experiences, all historical data must be accessible for analysis.

Incorrect:

Predictive Analytics
This works in tandеm with dеscriptivе analytics, which allows you to forеcast the behavior of your customers based on past data. This allows you to prеparе for specific customеr intеractions and improvе customеr rеtеntion. For еxamplе, you can usе historical transactions to prеdict how likely a customer is to rеnеw their subscription at a music plan. Thе nеxt timе that customеr walks into thе studio, your staff will rеcеivе an alеrt to offеr additional incеntivеs to pеrsuadе thеm to rеnеw.

Prescriptive Analytics
Prescriptive analytics finds solutions based on insights from descriptive analytics. For example, you can collect
data about remedial solutions to improve retention and see how well they performed. Prescriptive analytics forces you to retrospectively evaluate all strategies to improve them. For example, a bank might use Fraud Detection. An algorithm evaluates historical data after making a purchase to see if it matches the typical level of spending. If it detects an anomaly, the bank will be notified and will recommend a course of action, such as cancelling the bank card.

* Diagnostic Analytics
Diagnostic analytics involves the collection and examination of data pertaining to a particular issue or occurrence in order to comprehend the underlying causes. Consider a scenario where a fitness app, GymFit, observes a significant drop in user engagement during a specific period. Unraveling the factors contributing to this decline becomes the focal point of diagnostic analytics. In this context, GymFit delves into the data to uncover reasons why users might be disengaging, such as changes in workout preferences, dissatisfaction with features, or scheduling conflicts. Through careful analysis, GymFit identifies patterns and root causes behind the drop in user engagement. Armed with this knowledge, the fitness app can then implement targeted improvements, addressing concerns and enhancing the overall user experience to prevent further disengagement and attract new users.


Reference:

https://emergingindiagroup.com/data-analytics-for-customer-retention/



You have a Fabric workspace named Workspace1 that contains a dataflow named Dataflow1. Dataflow1 returns 500 rows of data.

You need to identify the min and max values for each column in the query results.

Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

  1. Show column value distribution
  2. Enable column profile
  3. Show column profile in details pane
  4. Show column quality details
  5. Enable details pane

Answer(s): B,E



You have a Fabric tenant that contains a Microsoft Power BI report. You are exploring a new semantic model.

You need to display the following column statistics:
Count Average Null count
Distinct count Standard deviation
Which Power Query function should you run?

  1. Table.schema
  2. Table.view
  3. Table.FuzzyGroup
  4. Table.Profile

Answer(s): D

Explanation:

Power Query M, Table.Profile Syntax
Table.Profile(table as table, optional additionalAggregates as nullable list) as table
About
Returns a profile for the columns in table.

The following information is returned for each column (when applicable): minimum
maximum average
standard deviation count
null count distinct count


Reference:

https://learn.microsoft.com/en-us/powerquery-m/table-profile




Case Study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.

To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.

Existing Environment Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.

Data Environment
Contoso has the following data environment:
The Sales division uses a Microsoft Power BI Premium capacity.

The semantic model of the Online Sales department includes a fact table named Orders that uses Import mode. In the system of origin, the OrderID value represents the sequence in which orders are created.

The Research department uses an on-premises, third-party data warehousing product. Fabric is enabled for contoso.com.

An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.

A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.

Requirements Planned Changes
Contoso plans to make the following changes:
Enable support for Fabric in the Power BI Premium capacity used by the Sales division. Make all the data for the Sales division and the Research division available in Fabric.

For the Research division, create two Fabric workspaces named Productline1ws and Productline2ws. In Productline1ws, create a lakehouse named Lakehouse1.

In Lakehouse1, create a shortcut to storage1 named ResearchProduct.

Data Analytics Requirements
Contoso identifies the following data analytics requirements:
All the workspaces for the Sales division and the Research division must support all Fabric experiences. The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.

For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.

For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.

All the semantic models and reports for the Research division must use version control that supports branching.

Data Preparation Requirements
Contoso identifies the following data preparation requirements:
The Research division data for Productline2 must be retrieved from Lakehouse1 by using Fabric notebooks.

All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.

Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models: The number of rows added to the Orders table during refreshes must be minimized.

The semantic models in the Research division workspaces must use Direct Lake mode.

General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions: Follow the principle of least privilege when applicable.

Minimize implementation and maintenance effort when possible.

You need to recommend which type of Fabric capacity SKU meets the data analytics requirements for the Research division.

What should you recommend?

  1. A
  2. EM
  3. P
  4. F

Answer(s): D

Explanation:

Use F SKU for Fabric.

NOTE: Power BI embedded analytics requires a capacity (A, EM, P, or F SKU) in order to publish embedded Power BI content.

Microsoft Fabric
Microsoft Fabric is an Azure offering that brings together new and existing components from Power BI, Azure Synapse, and Azure Data Explorer into a single integrated environment. Fabric uses F SKUs and supports embedding Power BI items.

Scenario:

Data Analytics Requirements
Contoso identifies the following data analytics requirements:
*-> All the workspaces for the Sales division and the Research division must support all Fabric experiences. The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.

For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.

For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.

All the semantic models and reports for the Research division must use version control that supports branching.


Reference:

https://learn.microsoft.com/en-us/power-bi/developer/embedded/embedded-capacity




Case Study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.

To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.

Existing Environment Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.

Data Environment
Contoso has the following data environment:
The Sales division uses a Microsoft Power BI Premium capacity.

The semantic model of the Online Sales department includes a fact table named Orders that uses Import mode. In the system of origin, the OrderID value represents the sequence in which orders are created.

The Research department uses an on-premises, third-party data warehousing product. Fabric is enabled for contoso.com.

An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.

A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.

Requirements Planned Changes
Contoso plans to make the following changes:
Enable support for Fabric in the Power BI Premium capacity used by the Sales division. Make all the data for the Sales division and the Research division available in Fabric.

For the Research division, create two Fabric workspaces named Productline1ws and Productline2ws. In Productline1ws, create a lakehouse named Lakehouse1.

In Lakehouse1, create a shortcut to storage1 named ResearchProduct.

Data Analytics Requirements
Contoso identifies the following data analytics requirements:
All the workspaces for the Sales division and the Research division must support all Fabric experiences. The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing. The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.

For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.

For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.

All the semantic models and reports for the Research division must use version control that supports branching.

Data Preparation Requirements
Contoso identifies the following data preparation requirements:
The Research division data for Productline2 must be retrieved from Lakehouse1 by using Fabric notebooks.

All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.

Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models: The number of rows added to the Orders table during refreshes must be minimized.

The semantic models in the Research division workspaces must use Direct Lake mode.

General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions: Follow the principle of least privilege when applicable.

Minimize implementation and maintenance effort when possible.

HOTSPOT (Drag and Drop is not supported)
Which workspace role assignments should you recommend for ResearchReviewersGroup1 and ResearchReviewersGroup2? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Box 1: Viewer ResearchReviewersGroup1
For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.

Workspace roles in Lakehouse
Workspace roles define what user can do with Microsoft Fabric items. Roles can be assigned to individuals or security groups from workspace view. See, Give users access to workspaces.

The user can be assigned to the following roles:
Admin Member Contributor Viewer
In a lakehouse the users with Admin, Member, and Contributor roles can perform all CRUD (CREATE, READ, UPDATE and DELETE) operations on all data. A user with Viewer role can only read data stored in Tables using the SQL analytics endpoint.

Box 2: Contributor ResearchReviewersGroup2
For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.

Microsoft Fabric workspace roles


Etc.

Incorrect:
* Member
More permissions compared to Contributor
Scenario:
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.


Reference:

https://learn.microsoft.com/en-us/fabric/data-engineering/workspace-roles-lakehouse https://learn.microsoft.com/en-us/fabric/get-started/roles-workspaces



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